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SUMMARY:Reconstruction\, Trigger\, and Machine Learning for the HL-LHC
DTSTART;VALUE=DATE-TIME:20180426T140000Z
DTEND;VALUE=DATE-TIME:20180427T203000Z
DTSTAMP;VALUE=DATE-TIME:20190121T221637Z
UID:indico-event-714134@indico.cern.ch
DESCRIPTION:Recent developments in machine learning (ML) are rapidly chang
ing physics reconstruction algorithms. These are leading to better-perform
ing algorithms with faster computation times. In this workshop\, we invest
igate both hardware and algorithmic ML approaches to speed up inference in
data acquisition\, focusing on the HL-LHC trigger upgrade\, along with th
e potential to further compress the event information to produce smaller d
ata samples.\n\nThe goal of this workshop is to develop cross-experiment c
ollaborations to work on common problems that will be faced by HL-LHC expe
riments. Beyond this\, we hope to build collaborations between the HEP and
the CS communities. The irregular 3-D geometries of HEP detectors\, their
heterogeneity\, and the extremely small latencies provide unique and inte
resting data science challenges. Conversely\, we have excellent models of
how particles behave in our detectors\, and excellent simulations of these
detectors\, which makes obtaining vast training samples possible. By buil
ding HEP-CS collaborations\, we hope that HEP data sets can be used for cu
tting-edge ML research\, providing benefits to both communities.\n\n \n\n
All talks will be in the Kolker Room in Building 26 (26-414). See the M
IT campus map. The Kolker room is on the 4th floor. \n\nhttps://indico.ce
rn.ch/event/714134/
LOCATION:
URL:https://indico.cern.ch/event/714134/
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